Introduction: Cancer is caused by genetic abnormalities, such as mutation of ontogenesis or tumor suppressor genes which alter downstream signaling pathways and protein-protein interactions. Comparison of protein interactions in cancerous and normal cells can be of help in mechanisms of disease diagnoses and treatments.

Methods:

We constructed protein interaction networks of cancerous and normal cells. These protein interaction networks are constructed using gene-expression profiles measured from different samples of cancerous and normal tissues from four different parts of the body including colon, prostate, lung, and central nervous system. We used pattern recognition techniques to construct these networks. We calculated ten graph related parameters including closeness centrality, graph diameter, index of aggregation, entropy of edge distribution, connectivity, number of edges divided by the number of vertices, entropy, graph centrality, sum of the wiener number, and modified vertex distance numbers for each of the cancerous and normal protein interaction networks. We have also compared number of edges and hubs of the both cancerous and normal resultant protein interaction networks.

Results and Discussion:

Our results show that in the studied tissue samples, effective normal protein interaction networks are denser in number of edges and hubs compared with their corresponding effective cancerous protein interaction networks. Number of hubs in effective cancerous protein interaction networks decreases dramatically in comparison with normal tissues. This can be used as a symptom for identification of cancerous tissues.